Cloud Application

Massive data sets of continuous position-based queries (CPQs) in position monitoring applications offer a challenge of time ingestion while forwarding the position-based data within wireless search space area. As a result of recurrent modifications in network topology caused due to mobility of users, processing of large CPQ data sets and roaming CPQs is one of the challenges in position monitoring. Therefore, the parallel algorithm is proposed in this paper for clustering and parallel processing of roaming CPQs which will recognize solid clusters in the wireless search space area. We present an algorithm which proposes the use of search space areas for clustering and introduce a parallel framework for parallel processing of CPQ data sets. The wireless search space area from wireless networks are used for scalable and effective calculation of clusters and dimensions in wireless networks. Present continuous query processing techniques cannot competently process roaming CPQs in the wireless search space area. The proposed algorithm is demonstrated to have practically best speedups in processing roaming CPQs. Results indicate that the proposed work determine improved ability of CPQs over existing mechanisms and attain small query latency and high precision in position monitoring applications.

are sceptical to entrust their records to the cloud due to a number of reasons, such as lack of policy and legislative framework, lack of trust to the cloud storage, jurisdiction, legal implications, privacy, ownership and security risks. This study recommends that given the evolution of technology, the government should regulate cloud storage through policy and legislative promulgation, as well as developing a government-owned cloud managed through SITA in order for all government departments to use it. This study suggests a framework to migrate paperbased records to cloud storage that is controlled by the government.

The amount of traffic on web based social networks is very difficult to predict. In order to avoid wasting resources during low traffic periods or being overloaded during peak periods, it would be interesting to adapt the amount of resources dedicated to the service. In this work we detail the design and implementation of our own social network application, called Bwitter. Our first goal is to make Bwitter performance scales with the number of machines we dedicate to it. Our second goal is linked to our first one, we want to make Bwitter elastic so that it can react to flash crowds without suspending its services by adding resources in order to handle this load. To achieve the desired scalability and elasticity, Bwitter is implemented on a scalable key/value datastore with transactional capabilities running on the Cloud. During our tests we study the behaviour of Bwitter using the Scalaris datastore and having both running on Amazon's Elastic Compute Cloud. We show that the performance of Bwitter increases almost linearly with the number of resources we allocate to it. Bwitter is also able to improve its performance significantly in a matter of minutes.

The amount of traffic on web based social networks is very difficult to predict. In order to avoid wasting resources during low traffic periods or being overloaded during peak periods, it would be interesting to adapt the amount of resources dedicated to the service. In this work we detail the design and implementation of our own social network application, called Bwitter. Our first goal is to make Bwitter performance scales with the number of machines we dedicate to it. Our second goal is linked to our first one, we want to make Bwitter elastic so that it can react to flash crowds without suspending its services by adding resources in order to handle this load. To achieve the desired scalability and elasticity, Bwitter is implemented on a scalable key/value datastore with transactional capabilities running on the Cloud. During our tests we study the behaviour of Bwitter using the Scalaris datastore and having both running on Amazon's Elastic Compute Cloud. We show that the performance of Bwitter increases almost linearly with the number of resources we allocate to it. Bwitter is also able to improve its performance significantly in a matter of minutes.

The dilemma for domain experts and developers during design time of a cloud application is ensuring the sufficient programming abstractions between them in mapping the business requirements to cloud specifications. Thus, a modeling language is needed to capture and express the business requirements. Resources-Events-Agents (REA) is a well-known business requirement modeling language that decomposes the information system into three constituents with the set of compliant binary collaborations called, Duality. This study is a preliminary attempt to employ REA for developing cloud applications. In this study, we define a conceptual mapping between REA model and OASIS Topology and Orchestration Specification for cloud Applications (TOSCA) policies, plans and templates. Based on that, we proceed with the process of building business-driven cloud applications. In support of our model, we implement a cloud REA Modeling tool referred to as CREAM, where business requirements are specified in REA, then corresponding cloud application is composed and built. We describe the underlying mapping strategy as well as the details of our tool in support of the proposed approach.